ArticleNature methods2024
Analyzing single-cell bisulfite sequencing data with MethSCAn.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- SMORE: joint dimension reduction and cell population discovery on single-cell methylome data.bioRxiv : the preprint server for biology · 2026Article
- Trigger-day hCG effects on DNA methylation and neurodevelopment in ART offspring.Nature communications · 2026Article
- DNA methylation reprogramming in marsupial embryos is restricted to the extraembryonic lineage.Nature communications · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes.Communications biology · 2025Article
- Article
- Single-cell multi-omic detection of DNA methylation and histone modifications reconstructs the dynamics of epigenomic maintenance.Nature methods · 2025Article
- Systematic identification of regions where DNA methylation is correlated with transcription refines regulatory logic in normal and tumour tissues.Nucleic acids research · 2025Article
- Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective.Quantitative biology (Beijing, China) · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- AutoMethyc: an automated methylation analysis for massively parallel sequencing data.Briefings in bioinformatics · 2025Article
- Elucidating neuroepigenetic mechanisms to inform targeted therapeutics for brain disorders.iScience · 2025Review
- Deciphering differences in DNA methylation and transcriptome profiles of oocytes from pigs with high and low developmental competence.Environmental epigenetics · 2025Article
- vmrseq: probabilistic modeling of single-cell methylation heterogeneity.Genome biology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Single-cell bisulfite sequencing (scBS) is a technique that enables the assessment of DNA methylation at single-base pair and single-cell resolution. The analysis of large datasets obtained from scBS requires preprocessing to reduce the data size, improve the signal-to-noise ratio and provide interpretability. Typically, this is achieved by dividing the genome into large tiles and averaging the methylation signals within each tile. Here we demonstrate that this coarse-graining approach can lead to signal dilution. We propose improved strategies to identify more informative regions for methylation quantification and a more accurate quantitation method than simple averaging. Our approach enables better discrimination of cell types and other features of interest and reduces the need for large numbers of cells. We also present an approach to detect differentially methylated regions between groups of cells and demonstrate its ability to identify biologically meaningful regions that are associated with genes involved in the core functions of specific cell types. Finally, we present the software tool MethSCAn for scBS data analysis ( https://anders-biostat.github.io/MethSCAn ).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.